Finally figured out a way to port python packages to R using uv and reticulate: example with nnetsauce
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In this post, we will explore how to use nnetsauce in R. The updated code is available on GitHub: Techntonique/nnetsauce_r.
Install
# pip install uv # if necessary uv venv venv source venv/bin/activate uv pip install pip nnetsauce
From GitHub
install.packages("remotes")
remotes::install_github("Techtonique/nnetsauce_r")
Examples
Classification
library(nnetsauce)
set.seed(123)
X <- as.matrix(iris[, 1:4])
y <- as.integer(iris$Species) - 1L
(index_train <- base::sample.int(n = nrow(X),
size = floor(0.8*nrow(X)),
replace = FALSE))
X_train <- X[index_train, ]
y_train <- y[index_train]
X_test <- X[-index_train, ]
y_test <- y[-index_train]
obj <- nnetsauce::GLMClassifier(venv_path = "../venv")
obj$fit(X_train, y_train)
print(obj$score(X_test, y_test))
Regression
library(datasets) n <- 20 ; p <- 5 X <- matrix(rnorm(n * p), n, p) # no intercept! y <- rnorm(n) sklearn <- nnetsauce::get_sklearn(venv_path = "../venv") obj <- sklearn$tree$DecisionTreeRegressor() obj2 <- nnetsauce::RandomBagRegressor(obj, venv_path = "../venv") obj2$fit(X[1:12,], y[1:12]) print(sqrt(mean((obj2$predict(X[13:20, ]) - y[13:20])**2)))
AutoML
Regression
X <- MASS::Boston[,-14] # dataset has an ethical problem
y <- MASS::Boston$medv
set.seed(13)
(index_train <- base::sample.int(n = nrow(X),
size = floor(0.8*nrow(X)),
replace = FALSE))
X_train <- X[index_train, ]
y_train <- y[index_train]
X_test <- X[-index_train, ]
y_test <- y[-index_train]
obj <- LazyRegressor(venv_path = "../venv")
(res <- obj$fit(X_train, X_test, y_train, y_test))
## Adjusted R-Squared R-Squared
## DeepCustomRegressor(ExtraTreesRegressor) 0.9057805 0.91790778
## DeepCustomRegressor(BaggingRegressor) 0.8634080 0.88098916
## DeepCustomRegressor(RandomForestRegressor) 0.8511052 0.87026992
## DeepCustomRegressor(MLPRegressor) 0.8499194 0.86923669
## RandomForestRegressor 0.8448451 0.86481553
## DeepCustomRegressor(AdaBoostRegressor) 0.8005664 0.82623607
## DeepCustomRegressor(LinearRegression) 0.7472819 0.77981001
## DeepCustomRegressor(TransformedTargetRegressor) 0.7472819 0.77981001
## DeepCustomRegressor(LassoLarsIC) 0.7472695 0.77979918
## DeepCustomRegressor(RidgeCV) 0.7471410 0.77968718
## DeepCustomRegressor(LassoLarsCV) 0.7429694 0.77605256
## DeepCustomRegressor(LassoCV) 0.7428514 0.77594970
## DeepCustomRegressor(Ridge) 0.7420801 0.77527772
## DeepCustomRegressor(HuberRegressor) 0.7395445 0.77306849
## DeepCustomRegressor(ElasticNetCV) 0.7327859 0.76717982
## DeepCustomRegressor(BayesianRidge) 0.7314119 0.76598263
## DeepCustomRegressor(KNeighborsRegressor) 0.7154223 0.75205113
## DeepCustomRegressor(SGDRegressor) 0.7078489 0.74545254
## DeepCustomRegressor(LarsCV) 0.7019229 0.74028925
## DeepCustomRegressor(LinearSVR) 0.6968942 0.73590783
## DeepCustomRegressor(Lasso) 0.6535395 0.69813345
## DeepCustomRegressor(LassoLars) 0.6535340 0.69812867
## DeepCustomRegressor(ExtraTreeRegressor) 0.6211290 0.66989454
## DeepCustomRegressor(PassiveAggressiveRegressor) 0.6086479 0.65901998
## DeepCustomRegressor(DecisionTreeRegressor) 0.6065707 0.65721013
## DeepCustomRegressor(ElasticNet) 0.6049283 0.65577912
## DeepCustomRegressor(TweedieRegressor) 0.5796672 0.63376940
## DeepCustomRegressor(RANSACRegressor) 0.4054278 0.48195691
## DeepCustomRegressor(DummyRegressor) -0.1830116 -0.03074282
## DeepCustomRegressor(QuantileRegressor) -0.2675167 -0.10437103
## DeepCustomRegressor(Lars) -3.0377540 -2.51804307
## RMSE Time Taken
## DeepCustomRegressor(ExtraTreesRegressor) 2.711916 1.81884480
## DeepCustomRegressor(BaggingRegressor) 3.265265 0.43161893
## DeepCustomRegressor(RandomForestRegressor) 3.409146 2.49213696
## DeepCustomRegressor(MLPRegressor) 3.422695 2.50529003
## RandomForestRegressor 3.480075 1.14117718
## DeepCustomRegressor(AdaBoostRegressor) 3.945527 0.88237572
## DeepCustomRegressor(LinearRegression) 4.441442 0.10591102
## DeepCustomRegressor(TransformedTargetRegressor) 4.441442 0.09575891
## DeepCustomRegressor(LassoLarsIC) 4.441551 0.15964413
## DeepCustomRegressor(RidgeCV) 4.442681 0.09690094
## DeepCustomRegressor(LassoLarsCV) 4.479178 0.28806591
## DeepCustomRegressor(LassoCV) 4.480206 0.94069099
## DeepCustomRegressor(Ridge) 4.486920 0.08716798
## DeepCustomRegressor(HuberRegressor) 4.508921 0.30278468
## DeepCustomRegressor(ElasticNetCV) 4.567048 1.00011015
## DeepCustomRegressor(BayesianRidge) 4.578775 0.16070414
## DeepCustomRegressor(KNeighborsRegressor) 4.713096 0.14384484
## DeepCustomRegressor(SGDRegressor) 4.775398 0.10862613
## DeepCustomRegressor(LarsCV) 4.823588 0.26737404
## DeepCustomRegressor(LinearSVR) 4.864106 0.11280203
## DeepCustomRegressor(Lasso) 5.200352 0.11331511
## DeepCustomRegressor(LassoLars) 5.200393 0.17438006
## DeepCustomRegressor(ExtraTreeRegressor) 5.438155 0.23284817
## DeepCustomRegressor(PassiveAggressiveRegressor) 5.527003 0.13693190
## DeepCustomRegressor(DecisionTreeRegressor) 5.541652 0.35019112
## DeepCustomRegressor(ElasticNet) 5.553207 0.12111807
## DeepCustomRegressor(TweedieRegressor) 5.727994 0.10166979
## DeepCustomRegressor(RANSACRegressor) 6.812526 1.06024408
## DeepCustomRegressor(DummyRegressor) 9.609491 0.18867731
## DeepCustomRegressor(QuantileRegressor) 9.946785 0.46191096
## DeepCustomRegressor(Lars) 17.753165 0.15315008
Time series
set.seed(123)
X <- matrix(rnorm(300), 100, 3)
(index_train <- base::sample.int(n = nrow(X),
size = floor(0.8*nrow(X)),
replace = FALSE))
X_train <- data.frame(X[index_train, ])
X_test <- data.frame(X[-index_train, ])
obj <- LazyMTS(venv_path = "../venv")
(res <- obj$fit(X_train, X_test))
## RMSE MAE MPL Time Taken
## MTS(BayesianRidge) 0.9693231 0.7284321 0.3642161 0.41069007
## MTS(ElasticNetCV) 0.9857480 0.7467560 0.3733780 2.63610816
## MTS(LassoCV) 0.9899658 0.7509848 0.3754924 1.73795819
## MTS(DummyRegressor) 0.9907240 0.7519778 0.3759889 0.10515714
## MTS(ElasticNet) 0.9907240 0.7519778 0.3759889 0.13000989
## MTS(Lasso) 0.9907240 0.7519778 0.3759889 0.21233797
## MTS(LassoLarsCV) 0.9907240 0.7519778 0.3759889 0.85480905
## MTS(LassoLars) 0.9907240 0.7519778 0.3759889 0.19775724
## MTS(LarsCV) 0.9956436 0.7448679 0.3724340 1.20561934
## MTS(QuantileRegressor) 0.9966559 0.7577475 0.3788738 0.34696984
## MTS(RandomForestRegressor) 1.0050021 0.7788496 0.3894248 6.10781717
## VAR 1.0110766 0.7739979 0.3869989 0.03306627
## MTS(MLPRegressor) 1.0155043 0.7619689 0.3809844 0.64326286
## MTS(KNeighborsRegressor) 1.0204875 0.7723135 0.3861567 0.22889900
## MTS(AdaBoostRegressor) 1.0255662 0.7926204 0.3963102 2.82807398
## MTS(ExtraTreesRegressor) 1.0429340 0.7754142 0.3877071 6.00860429
## MTS(BaggingRegressor) 1.0568736 0.8238503 0.4119252 0.75960994
## MTS(TweedieRegressor) 1.0676467 0.8112648 0.4056324 0.45418596
## MTS(RidgeCV) 1.2321289 0.9610130 0.4805065 0.16912007
## MTS(ExtraTreeRegressor) 1.2463809 0.9800255 0.4900127 0.28515887
## VECM 1.3765455 1.1327719 0.5663860 0.02011490
## MTS(SGDRegressor) 1.4109004 1.1323320 0.5661660 0.15479517
## MTS(DecisionTreeRegressor) 1.4451682 1.1283590 0.5641795 0.19908309
## MTS(Ridge) 1.6345720 1.3245349 0.6622674 0.18555593
## MTS(PassiveAggressiveRegressor) 1.7938410 1.4594719 0.7297360 0.17754388
## MTS(TransformedTargetRegressor) 2.0591218 1.6801762 0.8400881 0.36497688
## MTS(LinearRegression) 2.0591218 1.6801762 0.8400881 0.18544602
## MTS(HuberRegressor) 2.0779168 1.6827919 0.8413959 1.01927090
## MTS(LinearSVR) 2.0784001 1.6831479 0.8415739 0.17197490
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